Job Description
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Company Overview
A leading global technology organization is developing a flexible and scalable AI foundation designed to power creative experiences across design, imaging, motion, and personalization. The organization is focused on building advanced AI infrastructure that enables real-time, customer-facing intelligence at scale while connecting machine learning innovation with production-grade distributed systems.
The AI Foundations team is seeking a Principal Architect to build and implement a comprehensive AI framework for a large-scale creative platform. The role combines strong machine learning expertise with distributed systems, data architecture, and large-scale service development.
The position will define and develop the end-to-end foundation supporting Agentic AI, Create AI, Imaging AI, Motion AI, and Personalization AI. This includes model orchestration, inference systems, data pipelines, caching and storage layers, session analytics, and continuous evaluation frameworks.
The successful candidate will combine applied research understanding with engineering leadership, connecting modeling innovation with reliable production systems that deliver real-time intelligence at scale.
Key Responsibilities
- Architect and evolve the complete AI technology stack covering Agentic AI, Construct AI, Imaging AI, Motion AI, and Personalization AI.
- Develop and operationalize end-to-end systems integrating microservices, data pipelines, LLM orchestration layers, internal and third-party models, databases, caches, session analytics, and evaluation systems.
- Develop large-scale data and inference infrastructure supporting model training, fine-tuning, evaluation, and deployment.
- Utilize distributed frameworks such as Spark, Kafka, and Flink to build scalable AI infrastructure.
- Develop high-performance runtime services for inference and orchestration with strong observability, fault tolerance, and latency guarantees.
- Apply effective caching and storage strategies to improve efficiency and cost-effectiveness across AI workloads.
- Lead the development of experimentation and evaluation systems incorporating session-level analytics, feedback loops, and quality metrics.
- Collaborate with applied research, product, and platform teams to integrate LLMs and other AI models into customer-facing services.
- Drive architectural strategy for AI Foundations by connecting machine learning models, reasoning engines, and data streams into adaptive and intelligent systems.
- Mentor senior engineers and scientists while promoting excellence in architecture, experimentation, and AI system development.
Required Qualifications
- 10+ years of experience in large-scale distributed systems, AI infrastructure, or ML platform engineering, with exposure to in-product AI application engineering.
- Proficiency in Python, Java, C++, or Go, with an emphasis on distributed systems, cloud-native deployment, performance tuning, and agentic AI application development.
- Hands-on experience with LLM orchestration frameworks, model routing, and multi-model inference.
- Deep understanding of machine learning and LLM fundamentals, including training, fine-tuning, deployment, and evaluation workflows.
- Familiarity with Agentic AI patterns, including reasoning loops, memory persistence, task decomposition, and multi-agent coordination.
- Ability to combine engineering precision with practical research understanding and connect prototyping with production implementation.
- Strong communication and collaboration skills, with experience influencing cross-functional technical direction.
- Proven expertise in building and scaling data pipelines, real-time streaming systems, and event-driven architectures using technologies such as Kafka, Spark, and Flink.
- Strong background in caching strategies, database development, and performance optimization for large-scale serving systems.
Preferred Qualifications
- Bachelor’s degree or equivalent experience in Computer Science, Data Science, Machine Learning, or a related technical field.
- Experience architecting AI assistants, build agents, or multimodal creative systems.
- Exposure to Generative AI technologies, including LLMs, diffusion models, or multimodal architectures.
- Experience with MLOps pipelines, feature stores, and model registries.
- Track record of open-source contributions, technical publications, or conference presentations.
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